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Open Source Computer Vision Library
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192 lines
8.4 KiB
192 lines
8.4 KiB
/*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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