+++ /dev/null
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-
-/*
- * cvhaartraining.h
- *
- * haar training functions
- */
-
-#ifndef _CVHAARTRAINING_H_
-#define _CVHAARTRAINING_H_
-
-/*
- * cvCreateTrainingSamples
- *
- * Create training samples applying random distortions to sample image and
- * store them in .vec file
- *
- * filename - .vec file name
- * imgfilename - sample image file name
- * bgcolor - background color for sample image
- * bgthreshold - background color threshold. Pixels those colors are in range
- * [bgcolor-bgthreshold, bgcolor+bgthreshold] are considered as transparent
- * bgfilename - background description file name. If not NULL samples
- * will be put on arbitrary background
- * count - desired number of samples
- * invert - if not 0 sample foreground pixels will be inverted
- * if invert == CV_RANDOM_INVERT then samples will be inverted randomly
- * maxintensitydev - desired max intensity deviation of foreground samples pixels
- * maxxangle - max rotation angles
- * maxyangle
- * maxzangle
- * showsamples - if not 0 samples will be shown
- * winwidth - desired samples width
- * winheight - desired samples height
- */
-#define CV_RANDOM_INVERT 0x7FFFFFFF
-
-void cvCreateTrainingSamples( const char* filename,
- const char* imgfilename, int bgcolor, int bgthreshold,
- const char* bgfilename, int count,
- int invert = 0, int maxintensitydev = 40,
- double maxxangle = 1.1,
- double maxyangle = 1.1,
- double maxzangle = 0.5,
- int showsamples = 0,
- int winwidth = 24, int winheight = 24 );
-
-void cvCreateTestSamples( const char* infoname,
- const char* imgfilename, int bgcolor, int bgthreshold,
- const char* bgfilename, int count,
- int invert, int maxintensitydev,
- double maxxangle, double maxyangle, double maxzangle,
- int showsamples,
- int winwidth, int winheight );
-
-/*
- * cvCreateTrainingSamplesFromInfo
- *
- * Create training samples from a set of marked up images and store them into .vec file
- * infoname - file in which marked up image descriptions are stored
- * num - desired number of samples
- * showsamples - if not 0 samples will be shown
- * winwidth - sample width
- * winheight - sample height
- *
- * Return number of successfully created samples
- */
-int cvCreateTrainingSamplesFromInfo( const char* infoname, const char* vecfilename,
- int num,
- int showsamples,
- int winwidth, int winheight );
-
-/*
- * cvShowVecSamples
- *
- * Shows samples stored in .vec file
- *
- * filename
- * .vec file name
- * winwidth
- * sample width
- * winheight
- * sample height
- * scale
- * the scale each sample is adjusted to
- */
-void cvShowVecSamples( const char* filename, int winwidth, int winheight, double scale );
-
-
-/*
- * cvCreateCascadeClassifier
- *
- * Create cascade classifier
- * dirname - directory name in which cascade classifier will be created.
- * It must exist and contain subdirectories 0, 1, 2, ... (nstages-1).
- * vecfilename - name of .vec file with object's images
- * bgfilename - name of background description file
- * npos - number of positive samples used in training of each stage
- * nneg - number of negative samples used in training of each stage
- * nstages - number of stages
- * numprecalculated - number of features being precalculated. Each precalculated feature
- * requires (number_of_samples*(sizeof( float ) + sizeof( short ))) bytes of memory
- * numsplits - number of binary splits in each weak classifier
- * 1 - stumps, 2 and more - trees.
- * minhitrate - desired min hit rate of each stage
- * maxfalsealarm - desired max false alarm of each stage
- * weightfraction - weight trimming parameter
- * mode - 0 - BASIC = Viola
- * 1 - CORE = All upright
- * 2 - ALL = All features
- * symmetric - if not 0 vertical symmetry is assumed
- * equalweights - if not 0 initial weights of all samples will be equal
- * winwidth - sample width
- * winheight - sample height
- * boosttype - type of applied boosting algorithm
- * 0 - Discrete AdaBoost
- * 1 - Real AdaBoost
- * 2 - LogitBoost
- * 3 - Gentle AdaBoost
- * stumperror - type of used error if Discrete AdaBoost algorithm is applied
- * 0 - misclassification error
- * 1 - gini error
- * 2 - entropy error
- */
-void cvCreateCascadeClassifier( const char* dirname,
- const char* vecfilename,
- const char* bgfilename,
- int npos, int nneg, int nstages,
- int numprecalculated,
- int numsplits,
- float minhitrate = 0.995F, float maxfalsealarm = 0.5F,
- float weightfraction = 0.95F,
- int mode = 0, int symmetric = 1,
- int equalweights = 1,
- int winwidth = 24, int winheight = 24,
- int boosttype = 3, int stumperror = 0 );
-
-void cvCreateTreeCascadeClassifier( const char* dirname,
- const char* vecfilename,
- const char* bgfilename,
- int npos, int nneg, int nstages,
- int numprecalculated,
- int numsplits,
- float minhitrate, float maxfalsealarm,
- float weightfraction,
- int mode, int symmetric,
- int equalweights,
- int winwidth, int winheight,
- int boosttype, int stumperror,
- int maxtreesplits, int minpos );
-
-#endif /* _CVHAARTRAINING_H_ */
projects / opencv / blobdiff