+++ /dev/null
-/*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.
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-// 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
- *
- * training of cascade of boosted classifiers based on haar features
- */
-
-#ifndef __CVHAARTRAINING_H_
-#define __CVHAARTRAINING_H_
-
-#include <_cvcommon.h>
-#include <cvclassifier.h>
-#include <string.h>
-#include <stdio.h>
-
-/* parameters for tree cascade classifier training */
-
-/* max number of clusters */
-#define CV_MAX_CLUSTERS 3
-
-/* term criteria for K-Means */
-#define CV_TERM_CRITERIA() cvTermCriteria( CV_TERMCRIT_EPS, 1000, 1E-5 )
-
-/* If CV_COL_ARRANGEMENT is defined then sample feature values are stored in column.
- Otherwise they are stored in row */
-#define CV_COL_ARRANGEMENT
-
-/* print statistic info */
-#define CV_VERBOSE 1
-
-#define CV_STAGE_CART_FILE_NAME "AdaBoostCARTHaarClassifier.txt"
-
-#define CV_HAAR_FEATURE_MAX 3
-#define CV_HAAR_FEATURE_DESC_MAX 20
-
-typedef int sum_type;
-typedef double sqsum_type;
-typedef short idx_type;
-
-#define CV_SUM_MAT_TYPE CV_32SC1
-#define CV_SQSUM_MAT_TYPE CV_64FC1
-#define CV_IDX_MAT_TYPE CV_16SC1
-
-#define CV_STUMP_TRAIN_PORTION 100
-
-#define CV_THRESHOLD_EPS (0.00001F)
-
-typedef struct CvTHaarFeature
-{
- char desc[CV_HAAR_FEATURE_DESC_MAX];
- int tilted;
- struct
- {
- CvRect r;
- float weight;
- } rect[CV_HAAR_FEATURE_MAX];
-} CvTHaarFeature;
-
-typedef struct CvFastHaarFeature
-{
- int tilted;
- struct
- {
- int p0, p1, p2, p3;
- float weight;
- } rect[CV_HAAR_FEATURE_MAX];
-} CvFastHaarFeature;
-
-typedef struct CvIntHaarFeatures
-{
- CvSize winsize;
- int count;
- CvTHaarFeature* feature;
- CvFastHaarFeature* fastfeature;
-} CvIntHaarFeatures;
-
-CV_INLINE CvTHaarFeature cvHaarFeature( const char* desc,
- int x0, int y0, int w0, int h0, float wt0,
- int x1, int y1, int w1, int h1, float wt1,
- int x2 CV_DEFAULT( 0 ), int y2 CV_DEFAULT( 0 ),
- int w2 CV_DEFAULT( 0 ), int h2 CV_DEFAULT( 0 ),
- float wt2 CV_DEFAULT( 0.0F ) );
-
-CV_INLINE CvTHaarFeature cvHaarFeature( const char* desc,
- int x0, int y0, int w0, int h0, float wt0,
- int x1, int y1, int w1, int h1, float wt1,
- int x2, int y2, int w2, int h2, float wt2 )
-{
- CvTHaarFeature hf;
-
- assert( CV_HAAR_FEATURE_MAX >= 3 );
- assert( strlen( desc ) < CV_HAAR_FEATURE_DESC_MAX );
-
- strcpy( &(hf.desc[0]), desc );
- hf.tilted = ( hf.desc[0] == 't' );
-
- hf.rect[0].r.x = x0;
- hf.rect[0].r.y = y0;
- hf.rect[0].r.width = w0;
- hf.rect[0].r.height = h0;
- hf.rect[0].weight = wt0;
-
- hf.rect[1].r.x = x1;
- hf.rect[1].r.y = y1;
- hf.rect[1].r.width = w1;
- hf.rect[1].r.height = h1;
- hf.rect[1].weight = wt1;
-
- hf.rect[2].r.x = x2;
- hf.rect[2].r.y = y2;
- hf.rect[2].r.width = w2;
- hf.rect[2].r.height = h2;
- hf.rect[2].weight = wt2;
-
- return hf;
-}
-
-/* Prepared for training samples */
-typedef struct CvHaarTrainingData
-{
- CvSize winsize; /* training image size */
- int maxnum; /* maximum number of samples */
- CvMat sum; /* sum images (each row represents image) */
- CvMat tilted; /* tilted sum images (each row represents image) */
- CvMat normfactor; /* normalization factor */
- CvMat cls; /* classes. 1.0 - object, 0.0 - background */
- CvMat weights; /* weights */
-
- CvMat* valcache; /* precalculated feature values (CV_32FC1) */
- CvMat* idxcache; /* presorted indices (CV_IDX_MAT_TYPE) */
-} CvHaarTrainigData;
-
-
-/* Passed to callback functions */
-typedef struct CvUserdata
-{
- CvHaarTrainingData* trainingData;
- CvIntHaarFeatures* haarFeatures;
-} CvUserdata;
-
-CV_INLINE
-CvUserdata cvUserdata( CvHaarTrainingData* trainingData,
- CvIntHaarFeatures* haarFeatures );
-
-CV_INLINE
-CvUserdata cvUserdata( CvHaarTrainingData* trainingData,
- CvIntHaarFeatures* haarFeatures )
-{
- CvUserdata userdata;
-
- userdata.trainingData = trainingData;
- userdata.haarFeatures = haarFeatures;
-
- return userdata;
-}
-
-
-#define CV_INT_HAAR_CLASSIFIER_FIELDS() \
- float (*eval)( CvIntHaarClassifier*, sum_type*, sum_type*, float ); \
- void (*save)( CvIntHaarClassifier*, FILE* file ); \
- void (*release)( CvIntHaarClassifier** );
-
-/* internal weak classifier*/
-typedef struct CvIntHaarClassifier
-{
- CV_INT_HAAR_CLASSIFIER_FIELDS()
-} CvIntHaarClassifier;
-
-/*
- * CART classifier
- */
-typedef struct CvCARTHaarClassifier
-{
- CV_INT_HAAR_CLASSIFIER_FIELDS()
-
- int count;
- int* compidx;
- CvTHaarFeature* feature;
- CvFastHaarFeature* fastfeature;
- float* threshold;
- int* left;
- int* right;
- float* val;
-} CvCARTHaarClassifier;
-
-/* internal stage classifier */
-typedef struct CvStageHaarClassifier
-{
- CV_INT_HAAR_CLASSIFIER_FIELDS()
-
- int count;
- float threshold;
- CvIntHaarClassifier** classifier;
-} CvStageHaarClassifier;
-
-/* internal cascade classifier */
-typedef struct CvCascadeHaarClassifier
-{
- CV_INT_HAAR_CLASSIFIER_FIELDS()
-
- int count;
- CvIntHaarClassifier** classifier;
-} CvCascadeHaarClassifier;
-
-
-/* internal tree cascade classifier node */
-typedef struct CvTreeCascadeNode
-{
- CvStageHaarClassifier* stage;
-
- struct CvTreeCascadeNode* next;
- struct CvTreeCascadeNode* child;
- struct CvTreeCascadeNode* parent;
-
- struct CvTreeCascadeNode* next_same_level;
- struct CvTreeCascadeNode* child_eval;
- int idx;
- int leaf;
-} CvTreeCascadeNode;
-
-/* internal tree cascade classifier */
-typedef struct CvTreeCascadeClassifier
-{
- CV_INT_HAAR_CLASSIFIER_FIELDS()
-
- CvTreeCascadeNode* root; /* root of the tree */
- CvTreeCascadeNode* root_eval; /* root node for the filtering */
-
- int next_idx;
-} CvTreeCascadeClassifier;
-
-
-CV_INLINE float cvEvalFastHaarFeature( CvFastHaarFeature* feature,
- sum_type* sum, sum_type* tilted )
-{
- sum_type* img = NULL;
- int i = 0;
- float ret = 0.0F;
-
- assert( feature );
-
- img = ( feature->tilted ) ? tilted : sum;
-
- assert( img );
-
- for( i = 0; feature->rect[i].weight != 0.0F && i < CV_HAAR_FEATURE_MAX; i++ )
- {
- ret += feature->rect[i].weight *
- ( img[feature->rect[i].p0] - img[feature->rect[i].p1] -
- img[feature->rect[i].p2] + img[feature->rect[i].p3] );
- }
-
- return ret;
-}
-
-typedef struct CvSampleDistortionData
-{
- IplImage* src;
- IplImage* erode;
- IplImage* dilate;
- IplImage* mask;
- IplImage* img;
- IplImage* maskimg;
- int dx;
- int dy;
- int bgcolor;
-} CvSampleDistortionData;
-
-/*
- * icvConvertToFastHaarFeature
- *
- * Convert to fast representation of haar features
- *
- * haarFeature - input array
- * fastHaarFeature - output array
- * size - size of arrays
- * step - row step for the integral image
- */
-void icvConvertToFastHaarFeature( CvTHaarFeature* haarFeature,
- CvFastHaarFeature* fastHaarFeature,
- int size, int step );
-
-
-void icvWriteVecHeader( FILE* file, int count, int width, int height );
-void icvWriteVecSample( FILE* file, CvArr* sample );
-void icvPlaceDistortedSample( CvArr* background,
- int inverse, int maxintensitydev,
- double maxxangle, double maxyangle, double maxzangle,
- int inscribe, double maxshiftf, double maxscalef,
- CvSampleDistortionData* data );
-void icvEndSampleDistortion( CvSampleDistortionData* data );
-
-int icvStartSampleDistortion( const char* imgfilename, int bgcolor, int bgthreshold,
- CvSampleDistortionData* data );
-
-typedef int (*CvGetHaarTrainingDataCallback)( CvMat* img, void* userdata );
-
-typedef struct CvVecFile
-{
- FILE* input;
- int count;
- int vecsize;
- int last;
- short* vector;
-} CvVecFile;
-
-int icvGetHaarTraininDataFromVecCallback( CvMat* img, void* userdata );
-
-/*
- * icvGetHaarTrainingDataFromVec
- *
- * Fill <data> with samples from .vec file, passed <cascade>
-int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int count,
- CvIntHaarClassifier* cascade,
- const char* filename,
- int* consumed );
- */
-
-CvIntHaarClassifier* icvCreateCARTHaarClassifier( int count );
-
-void icvReleaseHaarClassifier( CvIntHaarClassifier** classifier );
-
-void icvInitCARTHaarClassifier( CvCARTHaarClassifier* carthaar, CvCARTClassifier* cart,
- CvIntHaarFeatures* intHaarFeatures );
-
-float icvEvalCARTHaarClassifier( CvIntHaarClassifier* classifier,
- sum_type* sum, sum_type* tilted, float normfactor );
-
-CvIntHaarClassifier* icvCreateStageHaarClassifier( int count, float threshold );
-
-void icvReleaseStageHaarClassifier( CvIntHaarClassifier** classifier );
-
-float icvEvalStageHaarClassifier( CvIntHaarClassifier* classifier,
- sum_type* sum, sum_type* tilted, float normfactor );
-
-CvIntHaarClassifier* icvCreateCascadeHaarClassifier( int count );
-
-void icvReleaseCascadeHaarClassifier( CvIntHaarClassifier** classifier );
-
-float icvEvalCascadeHaarClassifier( CvIntHaarClassifier* classifier,
- sum_type* sum, sum_type* tilted, float normfactor );
-
-void icvSaveHaarFeature( CvTHaarFeature* feature, FILE* file );
-
-void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file );
-
-void icvSaveCARTHaarClassifier( CvIntHaarClassifier* classifier, FILE* file );
-
-CvIntHaarClassifier* icvLoadCARTHaarClassifier( FILE* file, int step );
-
-void icvSaveStageHaarClassifier( CvIntHaarClassifier* classifier, FILE* file );
-
-CvIntHaarClassifier* icvLoadCARTStageHaarClassifier( const char* filename, int step );
-
-
-/* tree cascade classifier */
-
-float icvEvalTreeCascadeClassifier( CvIntHaarClassifier* classifier,
- sum_type* sum, sum_type* tilted, float normfactor );
-
-void icvSetLeafNode( CvTreeCascadeClassifier* tree, CvTreeCascadeNode* leaf );
-
-float icvEvalTreeCascadeClassifierFilter( CvIntHaarClassifier* classifier, sum_type* sum,
- sum_type* tilted, float normfactor );
-
-CvTreeCascadeNode* icvCreateTreeCascadeNode();
-
-void icvReleaseTreeCascadeNodes( CvTreeCascadeNode** node );
-
-void icvReleaseTreeCascadeClassifier( CvIntHaarClassifier** classifier );
-
-/* Prints out current tree structure to <stdout> */
-void icvPrintTreeCascade( CvTreeCascadeNode* root );
-
-/* Loads tree cascade classifier */
-CvIntHaarClassifier* icvLoadTreeCascadeClassifier( const char* filename, int step,
- int* splits );
-
-/* Finds leaves belonging to maximal level and connects them via leaf->next_same_level */
-CvTreeCascadeNode* icvFindDeepestLeaves( CvTreeCascadeClassifier* tree );
-
-#endif /* __CVHAARTRAINING_H_ */
projects / opencv / blobdiff