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143 lines
3.9 KiB
ReStructuredText
143 lines
3.9 KiB
ReStructuredText
Soft Cascade Training
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=======================
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.. highlight:: cpp
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Soft Cascade Detector Training
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--------------------------------------------
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softcascade::Octave
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-------------------
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.. ocv:class:: softcascade::Octave : public Algorithm
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Public interface for soft cascade training algorithm. ::
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class Octave : public Algorithm
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{
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public:
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enum {
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// Direct backward pruning. (Cha Zhang and Paul Viola)
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DBP = 1,
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// Multiple instance pruning. (Cha Zhang and Paul Viola)
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MIP = 2,
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// Originally proposed by L. Bourdev and J. Brandt
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HEURISTIC = 4 };
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virtual ~Octave();
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static cv::Ptr<Octave> create(cv::Rect boundingBox, int npositives, int nnegatives, int logScale, int shrinkage);
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virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth) = 0;
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virtual void setRejectThresholds(OutputArray thresholds) = 0;
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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;
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};
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softcascade::Octave::~Octave
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---------------------------------------
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Destructor for Octave.
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.. ocv:function:: softcascade::Octave::~Octave()
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softcascade::Octave::train
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--------------------------
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.. ocv:function:: bool softcascade::Octave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)
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:param dataset an object that allows communicate for training set.
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:param pool an object that presents feature pool.
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:param weaks a number of weak trees should be trained.
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:param treeDepth a depth of resulting weak trees.
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softcascade::Octave::setRejectThresholds
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----------------------------------------
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.. ocv:function:: void softcascade::Octave::setRejectThresholds(OutputArray thresholds)
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:param thresholds an output array of resulted rejection vector. Have same size as number of trained stages.
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softcascade::Octave::write
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--------------------------
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.. 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
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:param fs an output file storage to store trained detector.
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:param pool an object that presents feature pool.
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:param dataset a rejection vector that should be included in detector xml file.
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:param name a name of root node for trained detector.
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softcascade::FeaturePool
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------------------------
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.. ocv:class:: softcascade::FeaturePool
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Public interface for feature pool. This is a hight level abstraction for training random feature pool. ::
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class FeaturePool
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{
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public:
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virtual int size() const = 0;
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virtual float apply(int fi, int si, const Mat& channels) const = 0;
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virtual void write( cv::FileStorage& fs, int index) const = 0;
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virtual ~FeaturePool();
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};
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softcascade::FeaturePool::size
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------------------------------
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Returns size of feature pool.
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.. ocv:function:: int softcascade::FeaturePool::size() const
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softcascade::FeaturePool::~FeaturePool
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--------------------------------------
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FeaturePool destructor.
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.. ocv:function:: softcascade::FeaturePool::~FeaturePool()
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softcascade::FeaturePool::write
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-------------------------------
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Write specified feature from feature pool to file storage.
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.. 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.
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:param index an index of feature that should be stored.
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softcascade::FeaturePool::apply
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-------------------------------
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Compute feature on integral channel image.
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.. 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.
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:param si an index of sample.
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:param fs a channel matrix. |