--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
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+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// Intel License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
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+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of Intel Corporation may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
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+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
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+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+/*
+ * 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
+ * bg_vecfile - true if bgfilename represents a vec file with discrete negatives
+ * 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, bool bg_vecfile = false );
+
+#endif /* _CVHAARTRAINING_H_ */
projects / opencv / blobdiff