mirror of
https://github.com/opencv/opencv.git
synced 2024-12-27 11:28:14 +08:00
193 lines
8.4 KiB
C
193 lines
8.4 KiB
C
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// 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.
|
|
//
|
|
// * 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.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// 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_ */
|