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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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/*
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* cvhaartraining.h
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*
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* haar training functions
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*/
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#ifndef _CVHAARTRAINING_H_
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#define _CVHAARTRAINING_H_
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/*
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* cvCreateTrainingSamples
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*
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* Create training samples applying random distortions to sample image and
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* store them in .vec file
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*
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* filename - .vec file name
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* imgfilename - sample image file name
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* bgcolor - background color for sample image
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* bgthreshold - background color threshold. Pixels those colors are in range
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* [bgcolor-bgthreshold, bgcolor+bgthreshold] are considered as transparent
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* bgfilename - background description file name. If not NULL samples
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* will be put on arbitrary background
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* count - desired number of samples
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* invert - if not 0 sample foreground pixels will be inverted
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* if invert == CV_RANDOM_INVERT then samples will be inverted randomly
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* maxintensitydev - desired max intensity deviation of foreground samples pixels
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* maxxangle - max rotation angles
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* maxyangle
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* maxzangle
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* showsamples - if not 0 samples will be shown
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* winwidth - desired samples width
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* winheight - desired samples height
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*/
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#define CV_RANDOM_INVERT 0x7FFFFFFF
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void cvCreateTrainingSamples( const char* filename,
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const char* imgfilename, int bgcolor, int bgthreshold,
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const char* bgfilename, int count,
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int invert = 0, int maxintensitydev = 40,
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double maxxangle = 1.1,
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double maxyangle = 1.1,
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double maxzangle = 0.5,
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int showsamples = 0,
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int winwidth = 24, int winheight = 24 );
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void cvCreateTestSamples( const char* infoname,
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const char* imgfilename, int bgcolor, int bgthreshold,
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const char* bgfilename, int count,
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int invert, int maxintensitydev,
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double maxxangle, double maxyangle, double maxzangle,
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int showsamples,
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int winwidth, int winheight );
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/*
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* cvCreateTrainingSamplesFromInfo
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*
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* Create training samples from a set of marked up images and store them into .vec file
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* infoname - file in which marked up image descriptions are stored
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* num - desired number of samples
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* showsamples - if not 0 samples will be shown
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* winwidth - sample width
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* winheight - sample height
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*
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* Return number of successfully created samples
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*/
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int cvCreateTrainingSamplesFromInfo( const char* infoname, const char* vecfilename,
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int num,
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int showsamples,
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int winwidth, int winheight );
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/*
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* cvShowVecSamples
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*
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* Shows samples stored in .vec file
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*
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* filename
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* .vec file name
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* winwidth
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* sample width
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* winheight
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* sample height
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* scale
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* the scale each sample is adjusted to
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*/
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void cvShowVecSamples( const char* filename, int winwidth, int winheight, double scale );
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/*
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* cvCreateCascadeClassifier
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*
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* Create cascade classifier
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* dirname - directory name in which cascade classifier will be created.
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* It must exist and contain subdirectories 0, 1, 2, ... (nstages-1).
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* vecfilename - name of .vec file with object's images
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* bgfilename - name of background description file
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* bg_vecfile - true if bgfilename represents a vec file with discrete negatives
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* npos - number of positive samples used in training of each stage
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* nneg - number of negative samples used in training of each stage
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* nstages - number of stages
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* numprecalculated - number of features being precalculated. Each precalculated feature
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* requires (number_of_samples*(sizeof( float ) + sizeof( short ))) bytes of memory
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* numsplits - number of binary splits in each weak classifier
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* 1 - stumps, 2 and more - trees.
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* minhitrate - desired min hit rate of each stage
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* maxfalsealarm - desired max false alarm of each stage
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* weightfraction - weight trimming parameter
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* mode - 0 - BASIC = Viola
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* 1 - CORE = All upright
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* 2 - ALL = All features
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* symmetric - if not 0 vertical symmetry is assumed
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* equalweights - if not 0 initial weights of all samples will be equal
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* winwidth - sample width
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* winheight - sample height
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* boosttype - type of applied boosting algorithm
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* 0 - Discrete AdaBoost
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* 1 - Real AdaBoost
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* 2 - LogitBoost
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* 3 - Gentle AdaBoost
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* stumperror - type of used error if Discrete AdaBoost algorithm is applied
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* 0 - misclassification error
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* 1 - gini error
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* 2 - entropy error
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*/
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void cvCreateCascadeClassifier( const char* dirname,
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const char* vecfilename,
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const char* bgfilename,
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int npos, int nneg, int nstages,
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int numprecalculated,
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int numsplits,
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float minhitrate = 0.995F, float maxfalsealarm = 0.5F,
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float weightfraction = 0.95F,
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int mode = 0, int symmetric = 1,
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int equalweights = 1,
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int winwidth = 24, int winheight = 24,
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int boosttype = 3, int stumperror = 0 );
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void cvCreateTreeCascadeClassifier( const char* dirname,
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const char* vecfilename,
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const char* bgfilename,
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int npos, int nneg, int nstages,
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int numprecalculated,
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int numsplits,
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float minhitrate, float maxfalsealarm,
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float weightfraction,
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int mode, int symmetric,
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int equalweights,
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int winwidth, int winheight,
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int boosttype, int stumperror,
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int maxtreesplits, int minpos, bool bg_vecfile = false );
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#endif /* _CVHAARTRAINING_H_ */
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